{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rock-paper-scissors.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJrQ0jDwqNRA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22a990b3-372d-4c6e-e304-0ce9173dea7c"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_datasets as tfds\n",
        "from tensorflow.keras import layers\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPMC3dPdsLVM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(training_set,validation_set),dataset_info = tfds.load('rock_paper_scissors',split=['train','test'],as_supervised=True,with_info=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSJn12zEtZHO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "27bb30e5-af4e-4367-efaa-b0f2f8a4dba8"
      },
      "source": [
        "dataset_info"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='rock_paper_scissors',\n",
              "    version=3.0.0,\n",
              "    description='Images of hands playing rock, paper, scissor game.',\n",
              "    homepage='http://laurencemoroney.com/rock-paper-scissors-dataset',\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(300, 300, 3), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),\n",
              "    }),\n",
              "    total_num_examples=2892,\n",
              "    splits={\n",
              "        'test': 372,\n",
              "        'train': 2520,\n",
              "    },\n",
              "    supervised_keys=('image', 'label'),\n",
              "    citation=\"\"\"@ONLINE {rps,\n",
              "    author = \"Laurence Moroney\",\n",
              "    title = \"Rock, Paper, Scissors Dataset\",\n",
              "    month = \"feb\",\n",
              "    year = \"2019\",\n",
              "    url = \"http://laurencemoroney.com/rock-paper-scissors-dataset\"\n",
              "    }\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqokOi_dtYlt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "1a3ccfd7-23a0-4573-cdaa-861551be050f"
      },
      "source": [
        "num_classes = dataset_info.features['label'].num_classes\n",
        "labels = dataset_info.features['label'].names\n",
        "\n",
        "num_training_examples = 0\n",
        "num_validation_examples = 0\n",
        "\n",
        "for example in training_set:\n",
        "  num_training_examples += 1\n",
        "\n",
        "for example in validation_set:\n",
        "  num_validation_examples += 1\n",
        "\n",
        "print(f'Total number of classes are {num_classes}')\n",
        "print(f'The labels of dataset are {labels}')\n",
        "print(f'Training examples: {num_training_examples} and Validation examples: {num_validation_examples}')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total number of classes are 3\n",
            "The labels of dataset are ['rock', 'paper', 'scissors']\n",
            "Training examples: 2520 and Validation examples: 372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7fxHSh_u_J3",
        "colab_type": "text"
      },
      "source": [
        "**Reformating the image to apply MobileNet V2 (224 X 224)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjOzPkVhu0d8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_res = 224\n",
        "\n",
        "batch_size = 32\n",
        "\n",
        "def format_image(image,label):\n",
        "  image = tf.image.resize(image,(image_res, image_res))/255.0\n",
        "  return image,label\n",
        "\n",
        "train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(batch_size).prefetch(1)\n",
        "\n",
        "validation_batches = validation_set.map(format_image).batch(batch_size).prefetch(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7V1axngswOow",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = \"https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\"\n",
        "\n",
        "feature_extractor = hub.KerasLayer(url,input_shape = (image_res,image_res,3))\n",
        "\n",
        "#Freezing the pre-trained model by turning off trainable variable\n",
        "\n",
        "feature_extractor.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "282TulNAwvYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ffbf5efa-a4a2-497b-8ac3-d37bf0be9a27"
      },
      "source": [
        "#Adding the last layer with pre-trained model\n",
        "\n",
        "model = tf.keras.Sequential([feature_extractor,layers.Dense(num_classes)])\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer (KerasLayer)     (None, 1280)              2257984   \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 3)                 3843      \n",
            "=================================================================\n",
            "Total params: 2,261,827\n",
            "Trainable params: 3,843\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dsCKpNdxPnQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "9a6891b0-2a81-4303-ea88-40bc4f029de7"
      },
      "source": [
        "#Fitting the model\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics = ['accuracy'])\n",
        "epochs = 6\n",
        "\n",
        "history = model.fit(train_batches, epochs=epochs, validation_data=validation_batches)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "79/79 [==============================] - 13s 160ms/step - loss: 0.2944 - accuracy: 0.9210 - val_loss: 0.3966 - val_accuracy: 0.8306\n",
            "Epoch 2/6\n",
            "79/79 [==============================] - 8s 106ms/step - loss: 0.0568 - accuracy: 0.9980 - val_loss: 0.3798 - val_accuracy: 0.8253\n",
            "Epoch 3/6\n",
            "79/79 [==============================] - 8s 106ms/step - loss: 0.0303 - accuracy: 0.9996 - val_loss: 0.3749 - val_accuracy: 0.8118\n",
            "Epoch 4/6\n",
            "79/79 [==============================] - 8s 108ms/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.3088 - val_accuracy: 0.8710\n",
            "Epoch 5/6\n",
            "79/79 [==============================] - 8s 106ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.3518 - val_accuracy: 0.8226\n",
            "Epoch 6/6\n",
            "79/79 [==============================] - 8s 106ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.3665 - val_accuracy: 0.8118\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W78_1rX8x5k0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "075be628-a880-4d12-9ed6-7a3aafef1c8e"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(epochs_range,acc,label='Training Accuracy')\n",
        "plt.plot(epochs_range,val_acc,label='Validation Accuracy')\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.title('Training vs Validation Accuracy')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(epochs_range, loss, label='Training loss')\n",
        "plt.plot(epochs_range,val_loss,label = 'Validation loss')\n",
        "plt.legend(loc = 'upper right')\n",
        "plt.title('Training vs Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bbzN69GgWLVrEnDlzmDp1KrfffjtTpkyJ6HlroppnEZH40QXYEvJ6q7+skpkNA7o55+Yc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\nAMAkzwAAMMkzAABMAXlfLH9sHXWOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgvjiIpq3_kD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "59551bef-5243-4324-864a-2234b29eae56"
      },
      "source": [
        "#creating batch and making predictons\n",
        "labels = np.array(labels)\n",
        "\n",
        "labels"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['rock', 'paper', 'scissors'], dtype='<U8')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0qi2OWn4Osw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "ce0ea80e-b4cb-4ec4-d159-b5dcf2924588"
      },
      "source": [
        "image_batch, label_batch = next(iter(train_batches))\n",
        "\n",
        "image_batch = image_batch.numpy()\n",
        "label_batch = label_batch.numpy()\n",
        "\n",
        "predicted_batch = model.predict(image_batch)\n",
        "predicted_batch = tf.squeeze(predicted_batch).numpy()\n",
        "\n",
        "predicted_ids = np.argmax(predicted_batch,axis=1)\n",
        "predicted_class_names = labels[predicted_ids]\n",
        "\n",
        "print(predicted_class_names)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['rock' 'scissors' 'paper' 'paper' 'paper' 'paper' 'rock' 'paper' 'rock'\n",
            " 'scissors' 'paper' 'rock' 'rock' 'rock' 'paper' 'rock' 'rock' 'scissors'\n",
            " 'rock' 'scissors' 'scissors' 'paper' 'paper' 'rock' 'paper' 'paper'\n",
            " 'rock' 'scissors' 'scissors' 'paper' 'paper' 'paper']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qoag61m45lr_",
        "colab_type": "text"
      },
      "source": [
        "**Compare True labels and Predicted labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6u-jCIr5VUG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c30ed538-b165-468e-dc82-6ca17e99e1cb"
      },
      "source": [
        "print('Labels: ',label_batch)\n",
        "print('Predictions: ',predicted_ids)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels:  [0 2 1 1 1 1 0 1 0 2 1 0 0 0 1 0 0 2 0 2 2 1 1 0 1 1 0 2 2 1 1 1]\n",
            "Predictions:  [0 2 1 1 1 1 0 1 0 2 1 0 0 0 1 0 0 2 0 2 2 1 1 0 1 1 0 2 2 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k-mIo5R5yUY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "185924e3-2cf4-4a91-f270-304c115a180d"
      },
      "source": [
        "plt.figure(figsize =(12,8))\n",
        "for n in range(30):\n",
        "  plt.subplot(6,5,n+1)\n",
        "  plt.subplots_adjust(hspace=0.3)\n",
        "  plt.imshow(image_batch[n])\n",
        "  color = 'blue' if predicted_ids[n] == label_batch[n] else 'red'\n",
        "  plt.title(predicted_class_names[n].title(), color = color)\n",
        "  plt.axis('off')\n",
        "  _ = plt.suptitle('Model Predictions(blue: Correct, red: Incorrect)')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m1ex/RWIpHhDhqVpXfnLuJ0R4ECgAq2bwEi1ThJUVSys8H+RkxhS5pEKuALaKcA5wLfB+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\nKFCgQIGXKApDrkCBAgUKFChQ4CWK/w/Y6Bd+77rmvgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xal5O65p7TOP",
        "colab_type": "text"
      },
      "source": [
        "**Lets try with Inception V3 Architecture (299 X 299)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQPSPRh66xXT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "9e9a2e11-0402-43e4-8bf5-8ce13fdb29bc"
      },
      "source": [
        "image_resolution = 299\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "(training_set,validation_set),dataset_info = tfds.load('rock_paper_scissors',split=['train','test'],as_supervised=True,with_info=True)\n",
        "\n",
        "def image_format(image,label):\n",
        "  image = tf.image.resize(image,(image_resolution,image_resolution))/255.0\n",
        "  return image,label\n",
        "\n",
        "\n",
        "train_batches = training_set.shuffle(num_training_examples//4).map(image_format).batch(BATCH_SIZE).prefetch(1)\n",
        "validation_batches = validation_set.map(image_format).batch(BATCH_SIZE).prefetch(1)\n",
        "\n",
        "URL = \"https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4\"\n",
        "feature_extractor = hub.KerasLayer(URL,\n",
        "  input_shape=(image_resolution, image_resolution, 3),\n",
        "  trainable=False)\n",
        "\n",
        "model_inception = tf.keras.Sequential([\n",
        "  feature_extractor,\n",
        "  tf.keras.layers.Dense(num_classes)\n",
        "])\n",
        "\n",
        "model_inception.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_1 (KerasLayer)   (None, 2048)              21802784  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 21,808,931\n",
            "Trainable params: 6,147\n",
            "Non-trainable params: 21,802,784\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAlKEeRL7lEL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "6f1cb390-3778-430a-e01e-62df295fab4c"
      },
      "source": [
        "model_inception.compile(\n",
        "  optimizer='adam', \n",
        "  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "  metrics=['accuracy'])\n",
        "\n",
        "EPOCHS = 6\n",
        "\n",
        "history = model_inception.fit(train_batches,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_data=validation_batches)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "79/79 [==============================] - 17s 211ms/step - loss: 0.3915 - accuracy: 0.9194 - val_loss: 0.6882 - val_accuracy: 0.7473\n",
            "Epoch 2/6\n",
            "79/79 [==============================] - 11s 145ms/step - loss: 0.1051 - accuracy: 0.9909 - val_loss: 0.6029 - val_accuracy: 0.8065\n",
            "Epoch 3/6\n",
            "79/79 [==============================] - 11s 145ms/step - loss: 0.0589 - accuracy: 0.9952 - val_loss: 0.6548 - val_accuracy: 0.7661\n",
            "Epoch 4/6\n",
            "79/79 [==============================] - 11s 145ms/step - loss: 0.0403 - accuracy: 0.9976 - val_loss: 0.6129 - val_accuracy: 0.7957\n",
            "Epoch 5/6\n",
            "79/79 [==============================] - 11s 144ms/step - loss: 0.0289 - accuracy: 0.9980 - val_loss: 0.7079 - val_accuracy: 0.7581\n",
            "Epoch 6/6\n",
            "79/79 [==============================] - 11s 146ms/step - loss: 0.0227 - accuracy: 0.9984 - val_loss: 0.6872 - val_accuracy: 0.7823\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqavmCwH7m25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96-tg1zW77-7",
        "colab_type": "text"
      },
      "source": [
        "**Train with Resnet Feature Extractor (224 X 224)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3Do6SLA7_xu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "cc51edc5-bdb4-469a-fce1-70e3dd1f5a00"
      },
      "source": [
        "image_resolution = 224\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "(training_set,validation_set),dataset_info = tfds.load('rock_paper_scissors',split=['train','test'],as_supervised=True,with_info=True)\n",
        "\n",
        "def image_format(image,label):\n",
        "  image = tf.image.resize(image,(image_resolution,image_resolution))/255.0\n",
        "  return image,label\n",
        "\n",
        "\n",
        "train_batches = training_set.shuffle(num_training_examples//4).map(image_format).batch(BATCH_SIZE).prefetch(1)\n",
        "validation_batches = validation_set.map(image_format).batch(BATCH_SIZE).prefetch(1)\n",
        "\n",
        "URL = \"https://tfhub.dev/google/imagenet/resnet_v1_152/feature_vector/4\"\n",
        "feature_extractor = hub.KerasLayer(URL,\n",
        "  input_shape=(image_resolution, image_resolution, 3),\n",
        "  trainable=False)\n",
        "\n",
        "model_inception = tf.keras.Sequential([\n",
        "  feature_extractor,\n",
        "  tf.keras.layers.Dense(num_classes)\n",
        "])\n",
        "\n",
        "model_inception.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_2 (KerasLayer)   (None, 2048)              58295232  \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 58,301,379\n",
            "Trainable params: 6,147\n",
            "Non-trainable params: 58,295,232\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tI3nX1m58M3c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "6146aa81-b1b6-4fc9-e487-04ec417cace3"
      },
      "source": [
        "model_inception.compile(\n",
        "  optimizer='adam', \n",
        "  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "  metrics=['accuracy'])\n",
        "\n",
        "EPOCHS = 6\n",
        "\n",
        "history = model_inception.fit(train_batches,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_data=validation_batches)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "79/79 [==============================] - 21s 263ms/step - loss: 0.3109 - accuracy: 0.8921 - val_loss: 0.3606 - val_accuracy: 0.8441\n",
            "Epoch 2/6\n",
            "79/79 [==============================] - 11s 146ms/step - loss: 0.0393 - accuracy: 0.9992 - val_loss: 0.3288 - val_accuracy: 0.8522\n",
            "Epoch 3/6\n",
            "79/79 [==============================] - 11s 145ms/step - loss: 0.0219 - accuracy: 1.0000 - val_loss: 0.2935 - val_accuracy: 0.8629\n",
            "Epoch 4/6\n",
            "79/79 [==============================] - 11s 145ms/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.8656\n",
            "Epoch 5/6\n",
            "79/79 [==============================] - 12s 147ms/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.3059 - val_accuracy: 0.8656\n",
            "Epoch 6/6\n",
            "79/79 [==============================] - 12s 146ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.3412 - val_accuracy: 0.8575\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}